The five tools you will master
Together, these tools cover the full data analyst workflow: cleaning, querying, analysing, visualising and sharing.
Advanced Excel
Clean, analyse and report on data in the world’s most used tool.
Power BI
Build interactive dashboards and share them across a company.
Tableau
Turn data into clear, beautiful visual stories.
SQL
Ask questions of company databases and get answers fast.
Python for Data Analysis
Automate analysis and go deeper with code.
Course outline for each tool
Open any tool to see what you will study,this may not be all but 80% of what you'll learn, what you will be able to do at the end, and the sectors that use it.
Course outline
- Data cleaning, sorting, filtering and data validation
- Lookup functions: XLOOKUP, INDEX and MATCH
- Logical, text and date functions for real business data
- PivotTables, PivotCharts and slicers
- Power Query to import, clean and combine data
- Charts and interactive dashboards
- Conditional formatting and report design
- Using AI assistants to write and explain formulas
What you will gain
- Turn messy spreadsheets into clean, reliable data
- Summarise thousands of rows with PivotTables in minutes
- Build dashboards and reports that managers can read at a glance
- Automate repeat reporting with Power Query
Sectors that use it
- Banking and finance
- Retail and e-commerce
- Administration and HR
- Logistics and supply chain
Course outline
- Power BI Desktop and the Power BI Service
- Connecting to Excel, CSV, SQL and web data
- Cleaning and shaping data in Power Query
- Data modelling: tables, relationships and star schemas
- DAX measures and calculated columns
- Interactive reports: slicers, drill-through and tooltips
- Publishing, sharing and scheduled refresh basics
- AI features such as Q&A and Copilot
What you will gain
- Design professional, interactive dashboards
- Model data from several sources correctly
- Write DAX measures for KPIs such as growth and year-to-date totals
- Publish reports so teams can use them
Sectors that use it
- Banking and insurance
- Healthcare
- Telecommunications
- Government and public sector
Course outline
- Connecting to data and understanding dimensions and measures
- Core charts: bars, lines, maps, scatter plots and more
- Calculated fields, filters, sets and parameters
- Level of Detail (LOD) expressions, introduced step by step
- Building dashboards and data stories
- Design principles: colour, layout and clarity
- Publishing to Tableau Public and Tableau Cloud
- Presenting insights to non-technical audiences
What you will gain
- Create visuals that explain data quickly and clearly
- Build dashboards that let users explore the numbers
- Tell a story with data in meetings and reports
- Add a public Tableau portfolio to your profile
Sectors that use it
- Marketing, media and advertising
- E-commerce and retail
- Consulting and research
- NGOs and development organisations
Course outline
- How databases, tables, keys and relationships work
- SELECT, WHERE, ORDER BY and filtering data
- Aggregations: COUNT, SUM, AVG, GROUP BY and HAVING
- JOINs: combining data from many tables
- Subqueries and common table expressions (CTEs)
- Window functions for rankings and running totals
- Cleaning and transforming data with SQL
- Creating views, and using AI to write and explain queries
What you will gain
- Pull the exact data you need from large databases
- Answer business questions with clean, correct queries
- Prepare data for Power BI, Tableau and Python
- Read and understand other people’s queries
Sectors that use it
- Banking and fintech
- Telecommunications
- E-commerce and retail
- Technology companies and startups
Course outline
- Python basics: variables, loops, functions and data types
- Working in Jupyter Notebook and Google Colab
- Pandas: loading, cleaning and transforming data
- NumPy and basic statistics for analysts
- Visualisation with Matplotlib and Seaborn
- Reading data from Excel, CSV and SQL databases
- Exploratory data analysis (EDA) on real datasets
- Automating reports, with AI-assisted coding
What you will gain
- Clean and analyse large datasets that Excel struggles with
- Automate repetitive analysis and reporting
- Run exploratory analysis and present clear findings
- Build a project portfolio you can share on GitHub
Sectors that use it
- Fintech and banking
- Technology and startups
- Healthcare and research
- Oil, gas and energy
What you will have at the end of the course
You finish with tools you can use on day one, projects that prove your skills, and a clear plan for finding work.
- Hands-on skills in Advanced Excel, Power BI, Tableau, SQL and Python
- The ability to use AI tools to analyse data faster and check the results
- Real-dataset projects and a portfolio you can show to employers
- A professional certificate for your CV and LinkedIn profile
- Skills for roles such as data analyst, business intelligence analyst and reporting analyst
- Career guidance for your portfolio, CV and finding your first role or clients
Sectors where you will work
Every organisation that collects data needs people who can understand it. These are some of the sectors hiring data analysts.
Banking, finance and fintech
Track customers, risk, transactions and performance, and report to management.
Healthcare
Analyse patient flow, resources and outcomes to help hospitals and clinics run better.
Telecommunications
Study usage, churn and network data across millions of customers.
E-commerce and retail
Understand sales, stock, pricing and customer behaviour.
Oil, gas and energy
Monitor operations, costs and production with data-driven reports.
Logistics and supply chain
Optimise deliveries, routes, warehouses and suppliers.
Marketing, media and advertising
Measure campaigns, audiences and return on spend.
Government, education and NGOs
Use data to plan services, report results and support funding decisions.